Can survival prediction be improved by merging gene expression data sets?

ABSTRACT:

Background

High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample s

SUBMITTER: Yasrebi H 

PROVIDER: S-EPMC2761544 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature